[Hiring] Data Engineer, AI & Analytics @Power Digital

Role Description

You'll sit on the Data Team, which owns the core data foundation for Power Digital: the pipelines, modeling, and data marts that power our agency teams, clients, and AI initiatives. You'll work end to end, from raw ingestion through the semantic layer, using AI-agentic workflows as a normal part of how you build.

The data itself is the interesting part. Marketing data is fragmented by default. Every ad platform has its own API, its own schema, and its own definition of a conversion. Platforms restate attributed conversions days after the fact, each in a different way. Entity hierarchies don't match (campaign/ad set/ad on Meta, campaign/ad group/ad on Google). Naming conventions, currencies, and timezones vary by client.

We do this across a large client portfolio, each client with a different stack, in a warehouse with per-client tenancy. You'll work closely with Client Service, BI, Tagging & Tracking, Data Ops, and the nova product/engineering teams.

Key Responsibilities

• Design, build, and maintain the core data foundation (ingestion, modeling, and data marts).
• Own the workflow from raw platform data through the serving layers that support agency, client, internal, and AI consumers.
• Build ingestion that handles what ad platforms actually do: API changes, deprecated fields, aggressive rate limits, and retroactive restatement of conversion data.
• Model across sources to reconcile spend, impressions, conversions, and revenue across various platforms.
• Contribute to client-bespoke modeling on top of the core layer, including custom logic and client-specific marts.
• Build the semantic layers and metric definitions for AI-generated SQL.
• Use AI-agentic workflows to accelerate development and build intelligent data infrastructure.
• Collaborate cross-functionally with nova (product and engineering), AI/innovation, and client teams.
• Monitor and resolve data quality issues, and optimize pipelines for cost and performance.

Qualifications

• 3+ years in data or analytics engineering, including 1+ years owning a dbt project of meaningful size in production.
• Advanced proficiency in Python and SQL, focusing on production-grade code for data pipelines and modeling.
• Deep expertise in dbt, including incremental strategies, Jinja and macros, and managing large project DAGs.
• Strong command of Snowflake and the surrounding cloud data stack.
• Experience modeling in a multi-tenant environment.
• Working knowledge of marketing and advertising datasets.
• Proven experience designing and managing end-to-end data lifecycles.
• Familiarity with cloud-native infrastructure (GCP) and infrastructure-as-code principles.
• Real adoption of AI-agentic development workflows for coding, debugging, and system architecture.
• Demonstrated ability to architect AI-ready data models.
• Experience with Git and CI/CD best practices.
• Comfortable shipping iteratively and refining data products based on live feedback.

Requirements

• Agency, consultancy, or services experience is helpful but not required.
• Measurement work: incrementality, media mix modeling, attribution.
• Server-side tagging, or retail and marketplace data.

Key Performance Indicators (KPIs)

• AI-accelerated development: Reduce median development time by 20% within the first 6 months.
• Data quality and reliability: Maintain ≥99% accuracy and completeness across critical fields.
• Client request throughput: Deliver ≥90% of assigned client-bespoke modeling requests on time.
• Cross-functional enablement: Launch or materially migrate at least 2 major production data assets within the first 12 months.

Most Important Things (MITs)

• Build and ship end-to-end data systems that enable AI features.
• Deliver production-ready datasets and pipelines that unblock AI, product, and client teams.
• Reduce fragmentation by building unified, AI-ready data foundations.